How will a life course framework be used to tackle wider social determinants of health?
Bibliographic record
Abstract
The life course framework, proposed by Kuh and Schlomo in 1997, offers policy makers the means to understand the interaction between nature and nurture. This conceptual model illustrates how an individual's biological resources are influenced by their genetic endowment, their prenatal and postnatal development and their social and physical environment, both in early life and throughout the life course. Health is conceptualized as a dynamic process connecting biological and social elements that are affected by previous experiences and by present circumstances. Therefore, exposure at different stages of people's lives can either enhance or deplete the individual's health resources. Indeed, life course processes are of many kinds, including parent-child relationships, levels of social deprivation, the acquisition of emotional and behavioural assets in adolescence and the long-term effects of occupational hazards and work stress. The long-term effects of nature and nurture combine to influence disease outcomes. It is only in the last decade that theories, methods and new data have begun to be amalgamated, allowing us to further our understanding of health over the life course in ways that may eventually lead to more effective health policies and better health care. This article discusses life course concepts and how this framework can enlighten our understanding of wider social determinants of health, and provides a few examples of potential interventions to tackle their impact on health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".